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Quick Positional Health Assessment for Industrial Robot Prognostics and Health Management (PHM)

Published

Author(s)

Guixiu Qiao, Craig I. Schlenoff, Brian A. Weiss

Abstract

Robot calibration and performance will degrade if proper maintenance isn't performed. There have been challenges for manufacturers to optimize the maintenance strategy and minimize unexpected shutdowns. Prognostics and health management (PHM) can be applied to industrial robots through the development of performance metrics, test methods, reference datasets, and supporting tools. A subset of this research involves developing a quick health assessment methodology emphasizing the identification of static and dynamic positional accuracies. This methodology enables manufacturers to quickly assess the positional health of their robot systems. In this paper, the National Institute of Standards and Technology's (NIST) effort to develop the measurement science to support this development is presented, including the modeling and algorithm development for the test method, the advanced sensor development to measure 7-D information (time, X, Y, Z, roll, pitch, and yaw), algorithms to analyze the data, and a use case to present the results.
Proceedings Title
IEEE International Conference on Robotics and Automation 2017
Conference Dates
May 29-June 3, 2017
Conference Location
-

Keywords

industrial robot, prognostics and health management (PHM), quick health assessment, robot positional accuracy

Citation

Qiao, G. , Schlenoff, C. and Weiss, B. (2017), Quick Positional Health Assessment for Industrial Robot Prognostics and Health Management (PHM), IEEE International Conference on Robotics and Automation 2017, -, -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=922115 (Accessed September 23, 2021)
Created June 3, 2017, Updated October 20, 2017